In vehicle development CAE plays crucial role in arriving at optimum structural design to meet various vehicle performance targets in different domain such as Crash, NVH, Durability etc. Accurate CAE methodology can aid in reducing the number of physical tests & reducing overall vehicle development time. However, there are instances where there are gaps observed between test results and CAE predictions. These gaps get amplified in crash simulations as the event is highly dynamic and non-linear behavior simulation is always challenging. In order to enhance CAE methodology, it was decided to incorporate the effect of manufacturing and testing variations in crash CAE simulations. Manufacturing process accounts for variations due to inherent variation in material properties, spot weld nugget diameter, manufacturing processes such as stamping etc. whereas Physical Testing houses variation in barrier positions, test speed etc. within specified tolerance defined by regulatory bodies. These variations affect structural performance and negating these issues in early design phase will help to arrive at robust structural design.
FMVSS301 mandates fuel system integrity for vehicles post-crash, so a detailed assessment of the fuel system is needed to validate its integrity. Modeling the fuel tank assembly for vehicle crashworthiness is very challenging due to the fuel slosh phenomenon which occurs in the dynamic crash event. The fuel slosh behavior affects the overall dynamics of the fuel tank and its interaction with surrounding vehicle structures. Extensive studies can be found in the literature to improve fuel tank modeling. However, there is limited information related to modeling fluid’s free surface and the pressure profile imparted by the fluid on the tank during the crash event.
In this study, the pillars A, B and C from the Body-in-White (BIW) of a pickup passenger vehicle were considered, and the steels used for these components were identified based on the A2MAC1 platform, the SAEJ2947 standard, and state-of-the-art literature. Subsequently, these steels were compared with the client's product portfolio to propose a steel that meets the characteristics demanded by the automotive market for each of the components considered in the BIW. Next, the performance of each of the three pillars with these steels was validated and compared through crashworthiness simulations using Finite Element Analysis (FEA) with ANSYS LS-DYNA software. These simulations modeled the behavior of the pillars on side impact tests, with meshed parts based on the 2014 Chevrolet Silverado 1500 FEA model from the CCSA of the George Mason University. The impact speed was based on the Oblique pole side impact testing protocol from Euro NCAP; the time simulation was based on the Side impact Crashworthiness Evaluation 2.0 Rating Guidelines from the IIHS. To compare materials’ behavior, different curves were defined for each case. The tested materials were evaluated by comparing internal energy and displacement on each of the three pillars. Finally, results regarding the behavior of the different materials were discussed.
Application of Machine Learning technique to incorporate manufacturing and Testing variation for Robust BIW design for Crash performance